Skip to Main Content
 

Global Search Box

 
 
 
 

Files

File List


Dissertation - Behzad Abounia Omran.pdf (6.16 MB)

ETD Abstract Container

Abstract Header

Application of Data Mining and Big Data Analytics in the Construction Industry

Abounia Omran, Behzad
http://orcid.org/0000-0002-9264-8618
http://rave.ohiolink.edu/etdc/view?acc_num=osu148069742849934

Abstract Details

2016, Doctor of Philosophy, Ohio State University, Food, Agricultural and Biological Engineering.
In recent years, the digital world has experienced an explosion in the magnitude of data being captured and recorded in various industry fields. Accordingly, big data management has emerged to analyze and extract value out of the collected data. The traditional construction industry is also experiencing an increase in data generation and storage. However, its potential and ability for adopting big data techniques have not been adequately studied. This research investigates the trends of utilizing big data techniques in the construction research community, which eventually will impact construction practice. For this purpose, the application of 26 popular big data analysis techniques in six different construction research areas (represented by 30 prestigious construction journals) was reviewed. Trends, applications, and their associations in each of the six research areas were analyzed. Then, a more in-depth analysis was performed for two of the research areas including construction project management and computation and analytics in construction to map the associations and trends between different construction research subjects and selected analytical techniques. In the next step, the results from trend and subject analysis were used to identify a promising technique, Artificial Neural Network (ANN), for studying two construction-related subjects, including prediction of concrete properties and prediction of soil erosion quantity in highway slopes. This research also compared the performance and applicability of ANN against eight predictive modeling techniques commonly used by other industries in predicting the compressive strength of environmentally friendly concrete. The results of this research provide a comprehensive analysis of the current status of applying big data analytics techniques in construction research, including trends, frequencies, and usage distribution in six different construction-related research areas, and demonstrate the applicability and performance level of selected data analytics techniques with an emphasis on ANN in construction-related studies. The main purpose of this dissertation was to help practitioners and researchers identify a suitable and applicable data analytics technique for their specific construction/research issue(s) or to provide insights into potential research directions.
Qian Chen, Dr. (Advisor)
162 p.
Civil Engineering; Comparative Literature; Computer Science
Construction Industry; Big Data; Data Analytics; Data mining; Artificial Neural Network; ANN; Compressive Strength; Environmentally Friendly Concrete; Soil Erosion; Highway Slope; Predictive Modeling; Comparative Analysis

Recommended Citations

Citations

  • Abounia Omran, B. (2016). Application of Data Mining and Big Data Analytics in the Construction Industry [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu148069742849934

    APA Style (7th edition)

  • Abounia Omran, Behzad. Application of Data Mining and Big Data Analytics in the Construction Industry. 2016. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu148069742849934.

    MLA Style (8th edition)

  • Abounia Omran, Behzad. "Application of Data Mining and Big Data Analytics in the Construction Industry." Doctoral dissertation, Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu148069742849934

    Chicago Manual of Style (17th edition)

osu148069742849934
7,912
© 2016, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.